Improved Kernel Correlation Filtering Algorithm Integrating Scale Adaptation and Occlusion Redetection

To address the limitations of the Kernelized Correlation Filter (KCF) in handling scale variation and occlusion during visual tracking, this paper proposes a scale-adaptive and occlusion-robust KCF-based tracking method. The proposed approach integrates the Histogram of Oriented Gradients (HOGs) and Color Name (CN) features to fully exploit pixel-level information, thereby improving the accuracy of target localization. On this basis, a sub-region-based scale adaptation mechanism is introduced. Specifically, the target is partitioned into multiple sub-regions, and the KCF classifier is applied to each sub-region to estimate its center position. The relative displacement among these sub-region centers is then utilized to estimate target scale variation, enabling adaptive scale tracking. In addition, an occlusion-aware mechanism is designed to enhance robustness under occlusion. During tracking, occlusion detection is performed, and once occlusion is detected, template updating is suspended. Oriented FAST and Rotated BRIEF (ORB) features extracted from the template are subsequently matched with features from subsequent frames to re-acquire the target. Experimental results on the OTB2013 and OTB2015 benchmarks demonstrate that the proposed method achieves competitive precision and success rates compared with the baseline KCF and other representative trackers, while satisfying real-time tracking requirements using only CPU resources, indicating its practical applicability in resource-constrained environments.